
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")


df=pd.read_csv("2019.csv")
df.head()
def highlight_max(s):    
    is_max = s == s.max()
    return ['background-color: limegreen' if v else '' for v in is_max]
 
df.style.apply(highlight_max)


df.shape

(156, 9)


import seaborn as sns
sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x14085911640>


fig, axes = plt.subplots(nrows=2, ncols=2,constrained_layout=True,figsize=(12,8))

sns.barplot(x='GDP per capita',y='Country or region',data=df.nlargest(10,'GDP per capita'),ax=axes[0,0],palette="Blues_d")

sns.barplot(x='Social support' ,y='Country or region',data=df.nlargest(10,'Social support'),ax=axes[0,1],palette="YlGn")

sns.barplot(x='Healthy life expectancy' ,y='Country or region',data=df.nlargest(10,'Healthy life expectancy'),ax=axes[1,0],palette='OrRd')

sns.barplot(x='Freedom to make life choices' ,y='Country or region',data=df.nlargest(10,'Freedom to make life choices'),ax=axes[1,1],palette='YlOrBr')

<AxesSubplot:xlabel='Freedom to make life choices', ylabel='Country or region'>


fig, axes = plt.subplots(nrows=1, ncols=2,constrained_layout=True,figsize=(10,4))

sns.barplot(x='Generosity' ,y='Country or region',data=df.nlargest(10,'Generosity'),ax=axes[0],palette='Spectral')
sns.barplot(x='Perceptions of corruption' ,y='Country or region',data=df.nlargest(10,'Perceptions of corruption'),ax=axes[1],palette='RdYlGn')

<AxesSubplot:xlabel='Perceptions of corruption', ylabel='Country or region'>


print('max:',df['Score'].max())
print('min:',df['Score'].min())
add=df['Score'].max()-df['Score'].min()
grp=round(add/3,3)
print('range difference:',(grp))

max: 7.769
min: 2.853
range difference: 1.639


low=df['Score'].min()+grp
mid=low+grp
print('upper bound of Low grp',low)
print('upper bound of Mid grp',mid)
print('upper bound of High grp','max:',df['Score'].max())

upper bound of Low grp 4.492
upper bound of Mid grp 6.131
upper bound of High grp max: 7.769


cat=[]
for i in df.Score:
    if(i>0 and i<low):
        cat.append('Low')
        
        
    elif(i>low and i<mid):
         cat.append('Mid')
    else:
         cat.append('High')

df['Category']=cat


color = (df.Category == 'High' ).map({True: 'background-color: limegreen',False:'background-color: red'})
df.style.apply(lambda s: color)


df.loc[df['Country or region']=='India']


data={
    'Country or region':['Canada','US','UK','India'],
    'Score':[7.278,6.892,7.054,4.015],
    'GDP per capita':[1.365,1.433,1.333,0.755],
    'Social support':[1.505,1.457,1.538,0.765],
    'Healthy life expectancy':[1.039,0.874,0.996,0.588],
    'Freedom to make life choices':[0.584,0.454,0.45,0.498],
    'Generosity':[0.285,0.28,0.348,0.2],
    'Perceptions of corruption':[0.308,0.128,0.278,0.085]
}
d=pd.DataFrame(data)
d


ax = d.plot(y="Social support", x="Country or region", kind="bar",color='C3')
d.plot(y="GDP per capita", x="Country or region", kind="bar", ax=ax, color="C1")
d.plot(y="Healthy life expectancy", x="Country or region", kind="bar", ax=ax, color="C2")

plt.show()


ax = d.plot(y="Freedom to make life choices", x="Country or region", kind="bar",color='C3')
d.plot(y="Generosity", x="Country or region", kind="bar", ax=ax, color="C1",)
d.plot(y="Perceptions of corruption", x="Country or region", kind="bar", ax=ax, color="C2",)

plt.show()


conda install -c plotly plotly

Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: C:\Users\lenovo\anaconda3

  added / updated specs:
    - plotly


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    conda-4.10.3               |   py38haa95532_0         2.9 MB
    plotly-5.1.0               |             py_0         6.7 MB  plotly
    tenacity-7.0.0             |   py38haa95532_0          33 KB
    ------------------------------------------------------------
                                           Total:         9.6 MB

The following NEW packages will be INSTALLED:

  plotly             plotly/noarch::plotly-5.1.0-py_0
  tenacity           pkgs/main/win-64::tenacity-7.0.0-py38haa95532_0

The following packages will be UPDATED:

  conda                               4.10.1-py38haa95532_1 --> 4.10.3-py38haa95532_0


Downloading and Extracting Packages

conda-4.10.3         | 2.9 MB    |            |   0% 
conda-4.10.3         | 2.9 MB    | 3          |   3% 
conda-4.10.3         | 2.9 MB    | #          |  10% 
conda-4.10.3         | 2.9 MB    | #7         |  18% 
conda-4.10.3         | 2.9 MB    | ##5        |  26% 
conda-4.10.3         | 2.9 MB    | ###2       |  32% 
conda-4.10.3         | 2.9 MB    | ###9       |  39% 
conda-4.10.3         | 2.9 MB    | ####6      |  46% 
conda-4.10.3         | 2.9 MB    | #####3     |  54% 
conda-4.10.3         | 2.9 MB    | ######1    |  62% 
conda-4.10.3         | 2.9 MB    | ######8    |  69% 
conda-4.10.3         | 2.9 MB    | #######5   |  76% 
conda-4.10.3         | 2.9 MB    | ########2  |  83% 
conda-4.10.3         | 2.9 MB    | #########  |  90% 
conda-4.10.3         | 2.9 MB    | #########7 |  97% 
conda-4.10.3         | 2.9 MB    | ########## | 100% 

tenacity-7.0.0       | 33 KB     |            |   0% 
tenacity-7.0.0       | 33 KB     | ####8      |  49% 
tenacity-7.0.0       | 33 KB     | ########## | 100% 

plotly-5.1.0         | 6.7 MB    |            |   0% 
plotly-5.1.0         | 6.7 MB    | ########## | 100% 
plotly-5.1.0         | 6.7 MB    | ########## | 100% 
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done

Note: you may need to restart the kernel to use updated packages.


import plotly.graph_objs as go
from plotly.offline import iplot

data = dict(type = 'choropleth', 
           locations = df['Country or region'],
           locationmode = 'country names',
           colorscale='RdYlGn',
           z = df['Score'], 
           text = df['Country or region'],
           colorbar = {'title':'Happiness Score'})

layout = dict(title = 'Geographical Visualization of Happiness Score', 
              geo = dict(showframe = True, projection = {'type': 'azimuthal equal area'}))

choromap3 = go.Figure(data = [data], layout=layout)
iplot(choromap3)

	Overall rank	Country or region	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption
0	1	Finland	7.769000	1.340000	1.587000	0.986000	0.596000	0.153000	0.393000
1	2	Denmark	7.600000	1.383000	1.573000	0.996000	0.592000	0.252000	0.410000
2	3	Norway	7.554000	1.488000	1.582000	1.028000	0.603000	0.271000	0.341000
3	4	Iceland	7.494000	1.380000	1.624000	1.026000	0.591000	0.354000	0.118000
4	5	Netherlands	7.488000	1.396000	1.522000	0.999000	0.557000	0.322000	0.298000
5	6	Switzerland	7.480000	1.452000	1.526000	1.052000	0.572000	0.263000	0.343000
6	7	Sweden	7.343000	1.387000	1.487000	1.009000	0.574000	0.267000	0.373000
7	8	New Zealand	7.307000	1.303000	1.557000	1.026000	0.585000	0.330000	0.380000
8	9	Canada	7.278000	1.365000	1.505000	1.039000	0.584000	0.285000	0.308000
9	10	Austria	7.246000	1.376000	1.475000	1.016000	0.532000	0.244000	0.226000
10	11	Australia	7.228000	1.372000	1.548000	1.036000	0.557000	0.332000	0.290000
11	12	Costa Rica	7.167000	1.034000	1.441000	0.963000	0.558000	0.144000	0.093000
12	13	Israel	7.139000	1.276000	1.455000	1.029000	0.371000	0.261000	0.082000
13	14	Luxembourg	7.090000	1.609000	1.479000	1.012000	0.526000	0.194000	0.316000
14	15	United Kingdom	7.054000	1.333000	1.538000	0.996000	0.450000	0.348000	0.278000
15	16	Ireland	7.021000	1.499000	1.553000	0.999000	0.516000	0.298000	0.310000
16	17	Germany	6.985000	1.373000	1.454000	0.987000	0.495000	0.261000	0.265000
17	18	Belgium	6.923000	1.356000	1.504000	0.986000	0.473000	0.160000	0.210000
18	19	United States	6.892000	1.433000	1.457000	0.874000	0.454000	0.280000	0.128000
19	20	Czech Republic	6.852000	1.269000	1.487000	0.920000	0.457000	0.046000	0.036000
20	21	United Arab Emirates	6.825000	1.503000	1.310000	0.825000	0.598000	0.262000	0.182000
21	22	Malta	6.726000	1.300000	1.520000	0.999000	0.564000	0.375000	0.151000
22	23	Mexico	6.595000	1.070000	1.323000	0.861000	0.433000	0.074000	0.073000
23	24	France	6.592000	1.324000	1.472000	1.045000	0.436000	0.111000	0.183000
24	25	Taiwan	6.446000	1.368000	1.430000	0.914000	0.351000	0.242000	0.097000
25	26	Chile	6.444000	1.159000	1.369000	0.920000	0.357000	0.187000	0.056000
26	27	Guatemala	6.436000	0.800000	1.269000	0.746000	0.535000	0.175000	0.078000
27	28	Saudi Arabia	6.375000	1.403000	1.357000	0.795000	0.439000	0.080000	0.132000
28	29	Qatar	6.374000	1.684000	1.313000	0.871000	0.555000	0.220000	0.167000
29	30	Spain	6.354000	1.286000	1.484000	1.062000	0.362000	0.153000	0.079000
30	31	Panama	6.321000	1.149000	1.442000	0.910000	0.516000	0.109000	0.054000
31	32	Brazil	6.300000	1.004000	1.439000	0.802000	0.390000	0.099000	0.086000
32	33	Uruguay	6.293000	1.124000	1.465000	0.891000	0.523000	0.127000	0.150000
33	34	Singapore	6.262000	1.572000	1.463000	1.141000	0.556000	0.271000	0.453000
34	35	El Salvador	6.253000	0.794000	1.242000	0.789000	0.430000	0.093000	0.074000
35	36	Italy	6.223000	1.294000	1.488000	1.039000	0.231000	0.158000	0.030000
36	37	Bahrain	6.199000	1.362000	1.368000	0.871000	0.536000	0.255000	0.110000
37	38	Slovakia	6.198000	1.246000	1.504000	0.881000	0.334000	0.121000	0.014000
38	39	Trinidad & Tobago	6.192000	1.231000	1.477000	0.713000	0.489000	0.185000	0.016000
39	40	Poland	6.182000	1.206000	1.438000	0.884000	0.483000	0.117000	0.050000
40	41	Uzbekistan	6.174000	0.745000	1.529000	0.756000	0.631000	0.322000	0.240000
41	42	Lithuania	6.149000	1.238000	1.515000	0.818000	0.291000	0.043000	0.042000
42	43	Colombia	6.125000	0.985000	1.410000	0.841000	0.470000	0.099000	0.034000
43	44	Slovenia	6.118000	1.258000	1.523000	0.953000	0.564000	0.144000	0.057000
44	45	Nicaragua	6.105000	0.694000	1.325000	0.835000	0.435000	0.200000	0.127000
45	46	Kosovo	6.100000	0.882000	1.232000	0.758000	0.489000	0.262000	0.006000
46	47	Argentina	6.086000	1.092000	1.432000	0.881000	0.471000	0.066000	0.050000
47	48	Romania	6.070000	1.162000	1.232000	0.825000	0.462000	0.083000	0.005000
48	49	Cyprus	6.046000	1.263000	1.223000	1.042000	0.406000	0.190000	0.041000
49	50	Ecuador	6.028000	0.912000	1.312000	0.868000	0.498000	0.126000	0.087000
50	51	Kuwait	6.021000	1.500000	1.319000	0.808000	0.493000	0.142000	0.097000
51	52	Thailand	6.008000	1.050000	1.409000	0.828000	0.557000	0.359000	0.028000
52	53	Latvia	5.940000	1.187000	1.465000	0.812000	0.264000	0.075000	0.064000
53	54	South Korea	5.895000	1.301000	1.219000	1.036000	0.159000	0.175000	0.056000
54	55	Estonia	5.893000	1.237000	1.528000	0.874000	0.495000	0.103000	0.161000
55	56	Jamaica	5.890000	0.831000	1.478000	0.831000	0.490000	0.107000	0.028000
56	57	Mauritius	5.888000	1.120000	1.402000	0.798000	0.498000	0.215000	0.060000
57	58	Japan	5.886000	1.327000	1.419000	1.088000	0.445000	0.069000	0.140000
58	59	Honduras	5.860000	0.642000	1.236000	0.828000	0.507000	0.246000	0.078000
59	60	Kazakhstan	5.809000	1.173000	1.508000	0.729000	0.410000	0.146000	0.096000
60	61	Bolivia	5.779000	0.776000	1.209000	0.706000	0.511000	0.137000	0.064000
61	62	Hungary	5.758000	1.201000	1.410000	0.828000	0.199000	0.081000	0.020000
62	63	Paraguay	5.743000	0.855000	1.475000	0.777000	0.514000	0.184000	0.080000
63	64	Northern Cyprus	5.718000	1.263000	1.252000	1.042000	0.417000	0.191000	0.162000
64	65	Peru	5.697000	0.960000	1.274000	0.854000	0.455000	0.083000	0.027000
65	66	Portugal	5.693000	1.221000	1.431000	0.999000	0.508000	0.047000	0.025000
66	67	Pakistan	5.653000	0.677000	0.886000	0.535000	0.313000	0.220000	0.098000
67	68	Russia	5.648000	1.183000	1.452000	0.726000	0.334000	0.082000	0.031000
68	69	Philippines	5.631000	0.807000	1.293000	0.657000	0.558000	0.117000	0.107000
69	70	Serbia	5.603000	1.004000	1.383000	0.854000	0.282000	0.137000	0.039000
70	71	Moldova	5.529000	0.685000	1.328000	0.739000	0.245000	0.181000	0.000000
71	72	Libya	5.525000	1.044000	1.303000	0.673000	0.416000	0.133000	0.152000
72	73	Montenegro	5.523000	1.051000	1.361000	0.871000	0.197000	0.142000	0.080000
73	74	Tajikistan	5.467000	0.493000	1.098000	0.718000	0.389000	0.230000	0.144000
74	75	Croatia	5.432000	1.155000	1.266000	0.914000	0.296000	0.119000	0.022000
75	76	Hong Kong	5.430000	1.438000	1.277000	1.122000	0.440000	0.258000	0.287000
76	77	Dominican Republic	5.425000	1.015000	1.401000	0.779000	0.497000	0.113000	0.101000
77	78	Bosnia and Herzegovina	5.386000	0.945000	1.212000	0.845000	0.212000	0.263000	0.006000
78	79	Turkey	5.373000	1.183000	1.360000	0.808000	0.195000	0.083000	0.106000
79	80	Malaysia	5.339000	1.221000	1.171000	0.828000	0.508000	0.260000	0.024000
80	81	Belarus	5.323000	1.067000	1.465000	0.789000	0.235000	0.094000	0.142000
81	82	Greece	5.287000	1.181000	1.156000	0.999000	0.067000	0.000000	0.034000
82	83	Mongolia	5.285000	0.948000	1.531000	0.667000	0.317000	0.235000	0.038000
83	84	North Macedonia	5.274000	0.983000	1.294000	0.838000	0.345000	0.185000	0.034000
84	85	Nigeria	5.265000	0.696000	1.111000	0.245000	0.426000	0.215000	0.041000
85	86	Kyrgyzstan	5.261000	0.551000	1.438000	0.723000	0.508000	0.300000	0.023000
86	87	Turkmenistan	5.247000	1.052000	1.538000	0.657000	0.394000	0.244000	0.028000
87	88	Algeria	5.211000	1.002000	1.160000	0.785000	0.086000	0.073000	0.114000
88	89	Morocco	5.208000	0.801000	0.782000	0.782000	0.418000	0.036000	0.076000
89	90	Azerbaijan	5.208000	1.043000	1.147000	0.769000	0.351000	0.035000	0.182000
90	91	Lebanon	5.197000	0.987000	1.224000	0.815000	0.216000	0.166000	0.027000
91	92	Indonesia	5.192000	0.931000	1.203000	0.660000	0.491000	0.498000	0.028000
92	93	China	5.191000	1.029000	1.125000	0.893000	0.521000	0.058000	0.100000
93	94	Vietnam	5.175000	0.741000	1.346000	0.851000	0.543000	0.147000	0.073000
94	95	Bhutan	5.082000	0.813000	1.321000	0.604000	0.457000	0.370000	0.167000
95	96	Cameroon	5.044000	0.549000	0.910000	0.331000	0.381000	0.187000	0.037000
96	97	Bulgaria	5.011000	1.092000	1.513000	0.815000	0.311000	0.081000	0.004000
97	98	Ghana	4.996000	0.611000	0.868000	0.486000	0.381000	0.245000	0.040000
98	99	Ivory Coast	4.944000	0.569000	0.808000	0.232000	0.352000	0.154000	0.090000
99	100	Nepal	4.913000	0.446000	1.226000	0.677000	0.439000	0.285000	0.089000
100	101	Jordan	4.906000	0.837000	1.225000	0.815000	0.383000	0.110000	0.130000
101	102	Benin	4.883000	0.393000	0.437000	0.397000	0.349000	0.175000	0.082000
102	103	Congo (Brazzaville)	4.812000	0.673000	0.799000	0.508000	0.372000	0.105000	0.093000
103	104	Gabon	4.799000	1.057000	1.183000	0.571000	0.295000	0.043000	0.055000
104	105	Laos	4.796000	0.764000	1.030000	0.551000	0.547000	0.266000	0.164000
105	106	South Africa	4.722000	0.960000	1.351000	0.469000	0.389000	0.130000	0.055000
106	107	Albania	4.719000	0.947000	0.848000	0.874000	0.383000	0.178000	0.027000
107	108	Venezuela	4.707000	0.960000	1.427000	0.805000	0.154000	0.064000	0.047000
108	109	Cambodia	4.700000	0.574000	1.122000	0.637000	0.609000	0.232000	0.062000
109	110	Palestinian Territories	4.696000	0.657000	1.247000	0.672000	0.225000	0.103000	0.066000
110	111	Senegal	4.681000	0.450000	1.134000	0.571000	0.292000	0.153000	0.072000
111	112	Somalia	4.668000	0.000000	0.698000	0.268000	0.559000	0.243000	0.270000
112	113	Namibia	4.639000	0.879000	1.313000	0.477000	0.401000	0.070000	0.056000
113	114	Niger	4.628000	0.138000	0.774000	0.366000	0.318000	0.188000	0.102000
114	115	Burkina Faso	4.587000	0.331000	1.056000	0.380000	0.255000	0.177000	0.113000
115	116	Armenia	4.559000	0.850000	1.055000	0.815000	0.283000	0.095000	0.064000
116	117	Iran	4.548000	1.100000	0.842000	0.785000	0.305000	0.270000	0.125000
117	118	Guinea	4.534000	0.380000	0.829000	0.375000	0.332000	0.207000	0.086000
118	119	Georgia	4.519000	0.886000	0.666000	0.752000	0.346000	0.043000	0.164000
119	120	Gambia	4.516000	0.308000	0.939000	0.428000	0.382000	0.269000	0.167000
120	121	Kenya	4.509000	0.512000	0.983000	0.581000	0.431000	0.372000	0.053000
121	122	Mauritania	4.490000	0.570000	1.167000	0.489000	0.066000	0.106000	0.088000
122	123	Mozambique	4.466000	0.204000	0.986000	0.390000	0.494000	0.197000	0.138000
123	124	Tunisia	4.461000	0.921000	1.000000	0.815000	0.167000	0.059000	0.055000
124	125	Bangladesh	4.456000	0.562000	0.928000	0.723000	0.527000	0.166000	0.143000
125	126	Iraq	4.437000	1.043000	0.980000	0.574000	0.241000	0.148000	0.089000
126	127	Congo (Kinshasa)	4.418000	0.094000	1.125000	0.357000	0.269000	0.212000	0.053000
127	128	Mali	4.390000	0.385000	1.105000	0.308000	0.327000	0.153000	0.052000
128	129	Sierra Leone	4.374000	0.268000	0.841000	0.242000	0.309000	0.252000	0.045000
129	130	Sri Lanka	4.366000	0.949000	1.265000	0.831000	0.470000	0.244000	0.047000
130	131	Myanmar	4.360000	0.710000	1.181000	0.555000	0.525000	0.566000	0.172000
131	132	Chad	4.350000	0.350000	0.766000	0.192000	0.174000	0.198000	0.078000
132	133	Ukraine	4.332000	0.820000	1.390000	0.739000	0.178000	0.187000	0.010000
133	134	Ethiopia	4.286000	0.336000	1.033000	0.532000	0.344000	0.209000	0.100000
134	135	Swaziland	4.212000	0.811000	1.149000	0.000000	0.313000	0.074000	0.135000
135	136	Uganda	4.189000	0.332000	1.069000	0.443000	0.356000	0.252000	0.060000
136	137	Egypt	4.166000	0.913000	1.039000	0.644000	0.241000	0.076000	0.067000
137	138	Zambia	4.107000	0.578000	1.058000	0.426000	0.431000	0.247000	0.087000
138	139	Togo	4.085000	0.275000	0.572000	0.410000	0.293000	0.177000	0.085000
139	140	India	4.015000	0.755000	0.765000	0.588000	0.498000	0.200000	0.085000
140	141	Liberia	3.975000	0.073000	0.922000	0.443000	0.370000	0.233000	0.033000
141	142	Comoros	3.973000	0.274000	0.757000	0.505000	0.142000	0.275000	0.078000
142	143	Madagascar	3.933000	0.274000	0.916000	0.555000	0.148000	0.169000	0.041000
143	144	Lesotho	3.802000	0.489000	1.169000	0.168000	0.359000	0.107000	0.093000
144	145	Burundi	3.775000	0.046000	0.447000	0.380000	0.220000	0.176000	0.180000
145	146	Zimbabwe	3.663000	0.366000	1.114000	0.433000	0.361000	0.151000	0.089000
146	147	Haiti	3.597000	0.323000	0.688000	0.449000	0.026000	0.419000	0.110000
147	148	Botswana	3.488000	1.041000	1.145000	0.538000	0.455000	0.025000	0.100000
148	149	Syria	3.462000	0.619000	0.378000	0.440000	0.013000	0.331000	0.141000
149	150	Malawi	3.410000	0.191000	0.560000	0.495000	0.443000	0.218000	0.089000
150	151	Yemen	3.380000	0.287000	1.163000	0.463000	0.143000	0.108000	0.077000
151	152	Rwanda	3.334000	0.359000	0.711000	0.614000	0.555000	0.217000	0.411000
152	153	Tanzania	3.231000	0.476000	0.885000	0.499000	0.417000	0.276000	0.147000
153	154	Afghanistan	3.203000	0.350000	0.517000	0.361000	0.000000	0.158000	0.025000
154	155	Central African Republic	3.083000	0.026000	0.000000	0.105000	0.225000	0.235000	0.035000
155	156	South Sudan	2.853000	0.306000	0.575000	0.295000	0.010000	0.202000	0.091000

	Overall rank	Country or region	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption	Category
0	1	Finland	7.769000	1.340000	1.587000	0.986000	0.596000	0.153000	0.393000	High
1	2	Denmark	7.600000	1.383000	1.573000	0.996000	0.592000	0.252000	0.410000	High
2	3	Norway	7.554000	1.488000	1.582000	1.028000	0.603000	0.271000	0.341000	High
3	4	Iceland	7.494000	1.380000	1.624000	1.026000	0.591000	0.354000	0.118000	High
4	5	Netherlands	7.488000	1.396000	1.522000	0.999000	0.557000	0.322000	0.298000	High
5	6	Switzerland	7.480000	1.452000	1.526000	1.052000	0.572000	0.263000	0.343000	High
6	7	Sweden	7.343000	1.387000	1.487000	1.009000	0.574000	0.267000	0.373000	High
7	8	New Zealand	7.307000	1.303000	1.557000	1.026000	0.585000	0.330000	0.380000	High
8	9	Canada	7.278000	1.365000	1.505000	1.039000	0.584000	0.285000	0.308000	High
9	10	Austria	7.246000	1.376000	1.475000	1.016000	0.532000	0.244000	0.226000	High
10	11	Australia	7.228000	1.372000	1.548000	1.036000	0.557000	0.332000	0.290000	High
11	12	Costa Rica	7.167000	1.034000	1.441000	0.963000	0.558000	0.144000	0.093000	High
12	13	Israel	7.139000	1.276000	1.455000	1.029000	0.371000	0.261000	0.082000	High
13	14	Luxembourg	7.090000	1.609000	1.479000	1.012000	0.526000	0.194000	0.316000	High
14	15	United Kingdom	7.054000	1.333000	1.538000	0.996000	0.450000	0.348000	0.278000	High
15	16	Ireland	7.021000	1.499000	1.553000	0.999000	0.516000	0.298000	0.310000	High
16	17	Germany	6.985000	1.373000	1.454000	0.987000	0.495000	0.261000	0.265000	High
17	18	Belgium	6.923000	1.356000	1.504000	0.986000	0.473000	0.160000	0.210000	High
18	19	United States	6.892000	1.433000	1.457000	0.874000	0.454000	0.280000	0.128000	High
19	20	Czech Republic	6.852000	1.269000	1.487000	0.920000	0.457000	0.046000	0.036000	High
20	21	United Arab Emirates	6.825000	1.503000	1.310000	0.825000	0.598000	0.262000	0.182000	High
21	22	Malta	6.726000	1.300000	1.520000	0.999000	0.564000	0.375000	0.151000	High
22	23	Mexico	6.595000	1.070000	1.323000	0.861000	0.433000	0.074000	0.073000	High
23	24	France	6.592000	1.324000	1.472000	1.045000	0.436000	0.111000	0.183000	High
24	25	Taiwan	6.446000	1.368000	1.430000	0.914000	0.351000	0.242000	0.097000	High
25	26	Chile	6.444000	1.159000	1.369000	0.920000	0.357000	0.187000	0.056000	High
26	27	Guatemala	6.436000	0.800000	1.269000	0.746000	0.535000	0.175000	0.078000	High
27	28	Saudi Arabia	6.375000	1.403000	1.357000	0.795000	0.439000	0.080000	0.132000	High
28	29	Qatar	6.374000	1.684000	1.313000	0.871000	0.555000	0.220000	0.167000	High
29	30	Spain	6.354000	1.286000	1.484000	1.062000	0.362000	0.153000	0.079000	High
30	31	Panama	6.321000	1.149000	1.442000	0.910000	0.516000	0.109000	0.054000	High
31	32	Brazil	6.300000	1.004000	1.439000	0.802000	0.390000	0.099000	0.086000	High
32	33	Uruguay	6.293000	1.124000	1.465000	0.891000	0.523000	0.127000	0.150000	High
33	34	Singapore	6.262000	1.572000	1.463000	1.141000	0.556000	0.271000	0.453000	High
34	35	El Salvador	6.253000	0.794000	1.242000	0.789000	0.430000	0.093000	0.074000	High
35	36	Italy	6.223000	1.294000	1.488000	1.039000	0.231000	0.158000	0.030000	High
36	37	Bahrain	6.199000	1.362000	1.368000	0.871000	0.536000	0.255000	0.110000	High
37	38	Slovakia	6.198000	1.246000	1.504000	0.881000	0.334000	0.121000	0.014000	High
38	39	Trinidad & Tobago	6.192000	1.231000	1.477000	0.713000	0.489000	0.185000	0.016000	High
39	40	Poland	6.182000	1.206000	1.438000	0.884000	0.483000	0.117000	0.050000	High
40	41	Uzbekistan	6.174000	0.745000	1.529000	0.756000	0.631000	0.322000	0.240000	High
41	42	Lithuania	6.149000	1.238000	1.515000	0.818000	0.291000	0.043000	0.042000	High
42	43	Colombia	6.125000	0.985000	1.410000	0.841000	0.470000	0.099000	0.034000	Mid
43	44	Slovenia	6.118000	1.258000	1.523000	0.953000	0.564000	0.144000	0.057000	Mid
44	45	Nicaragua	6.105000	0.694000	1.325000	0.835000	0.435000	0.200000	0.127000	Mid
45	46	Kosovo	6.100000	0.882000	1.232000	0.758000	0.489000	0.262000	0.006000	Mid
46	47	Argentina	6.086000	1.092000	1.432000	0.881000	0.471000	0.066000	0.050000	Mid
47	48	Romania	6.070000	1.162000	1.232000	0.825000	0.462000	0.083000	0.005000	Mid
48	49	Cyprus	6.046000	1.263000	1.223000	1.042000	0.406000	0.190000	0.041000	Mid
49	50	Ecuador	6.028000	0.912000	1.312000	0.868000	0.498000	0.126000	0.087000	Mid
50	51	Kuwait	6.021000	1.500000	1.319000	0.808000	0.493000	0.142000	0.097000	Mid
51	52	Thailand	6.008000	1.050000	1.409000	0.828000	0.557000	0.359000	0.028000	Mid
52	53	Latvia	5.940000	1.187000	1.465000	0.812000	0.264000	0.075000	0.064000	Mid
53	54	South Korea	5.895000	1.301000	1.219000	1.036000	0.159000	0.175000	0.056000	Mid
54	55	Estonia	5.893000	1.237000	1.528000	0.874000	0.495000	0.103000	0.161000	Mid
55	56	Jamaica	5.890000	0.831000	1.478000	0.831000	0.490000	0.107000	0.028000	Mid
56	57	Mauritius	5.888000	1.120000	1.402000	0.798000	0.498000	0.215000	0.060000	Mid
57	58	Japan	5.886000	1.327000	1.419000	1.088000	0.445000	0.069000	0.140000	Mid
58	59	Honduras	5.860000	0.642000	1.236000	0.828000	0.507000	0.246000	0.078000	Mid
59	60	Kazakhstan	5.809000	1.173000	1.508000	0.729000	0.410000	0.146000	0.096000	Mid
60	61	Bolivia	5.779000	0.776000	1.209000	0.706000	0.511000	0.137000	0.064000	Mid
61	62	Hungary	5.758000	1.201000	1.410000	0.828000	0.199000	0.081000	0.020000	Mid
62	63	Paraguay	5.743000	0.855000	1.475000	0.777000	0.514000	0.184000	0.080000	Mid
63	64	Northern Cyprus	5.718000	1.263000	1.252000	1.042000	0.417000	0.191000	0.162000	Mid
64	65	Peru	5.697000	0.960000	1.274000	0.854000	0.455000	0.083000	0.027000	Mid
65	66	Portugal	5.693000	1.221000	1.431000	0.999000	0.508000	0.047000	0.025000	Mid
66	67	Pakistan	5.653000	0.677000	0.886000	0.535000	0.313000	0.220000	0.098000	Mid
67	68	Russia	5.648000	1.183000	1.452000	0.726000	0.334000	0.082000	0.031000	Mid
68	69	Philippines	5.631000	0.807000	1.293000	0.657000	0.558000	0.117000	0.107000	Mid
69	70	Serbia	5.603000	1.004000	1.383000	0.854000	0.282000	0.137000	0.039000	Mid
70	71	Moldova	5.529000	0.685000	1.328000	0.739000	0.245000	0.181000	0.000000	Mid
71	72	Libya	5.525000	1.044000	1.303000	0.673000	0.416000	0.133000	0.152000	Mid
72	73	Montenegro	5.523000	1.051000	1.361000	0.871000	0.197000	0.142000	0.080000	Mid
73	74	Tajikistan	5.467000	0.493000	1.098000	0.718000	0.389000	0.230000	0.144000	Mid
74	75	Croatia	5.432000	1.155000	1.266000	0.914000	0.296000	0.119000	0.022000	Mid
75	76	Hong Kong	5.430000	1.438000	1.277000	1.122000	0.440000	0.258000	0.287000	Mid
76	77	Dominican Republic	5.425000	1.015000	1.401000	0.779000	0.497000	0.113000	0.101000	Mid
77	78	Bosnia and Herzegovina	5.386000	0.945000	1.212000	0.845000	0.212000	0.263000	0.006000	Mid
78	79	Turkey	5.373000	1.183000	1.360000	0.808000	0.195000	0.083000	0.106000	Mid
79	80	Malaysia	5.339000	1.221000	1.171000	0.828000	0.508000	0.260000	0.024000	Mid
80	81	Belarus	5.323000	1.067000	1.465000	0.789000	0.235000	0.094000	0.142000	Mid
81	82	Greece	5.287000	1.181000	1.156000	0.999000	0.067000	0.000000	0.034000	Mid
82	83	Mongolia	5.285000	0.948000	1.531000	0.667000	0.317000	0.235000	0.038000	Mid
83	84	North Macedonia	5.274000	0.983000	1.294000	0.838000	0.345000	0.185000	0.034000	Mid
84	85	Nigeria	5.265000	0.696000	1.111000	0.245000	0.426000	0.215000	0.041000	Mid
85	86	Kyrgyzstan	5.261000	0.551000	1.438000	0.723000	0.508000	0.300000	0.023000	Mid
86	87	Turkmenistan	5.247000	1.052000	1.538000	0.657000	0.394000	0.244000	0.028000	Mid
87	88	Algeria	5.211000	1.002000	1.160000	0.785000	0.086000	0.073000	0.114000	Mid
88	89	Morocco	5.208000	0.801000	0.782000	0.782000	0.418000	0.036000	0.076000	Mid
89	90	Azerbaijan	5.208000	1.043000	1.147000	0.769000	0.351000	0.035000	0.182000	Mid
90	91	Lebanon	5.197000	0.987000	1.224000	0.815000	0.216000	0.166000	0.027000	Mid
91	92	Indonesia	5.192000	0.931000	1.203000	0.660000	0.491000	0.498000	0.028000	Mid
92	93	China	5.191000	1.029000	1.125000	0.893000	0.521000	0.058000	0.100000	Mid
93	94	Vietnam	5.175000	0.741000	1.346000	0.851000	0.543000	0.147000	0.073000	Mid
94	95	Bhutan	5.082000	0.813000	1.321000	0.604000	0.457000	0.370000	0.167000	Mid
95	96	Cameroon	5.044000	0.549000	0.910000	0.331000	0.381000	0.187000	0.037000	Mid
96	97	Bulgaria	5.011000	1.092000	1.513000	0.815000	0.311000	0.081000	0.004000	Mid
97	98	Ghana	4.996000	0.611000	0.868000	0.486000	0.381000	0.245000	0.040000	Mid
98	99	Ivory Coast	4.944000	0.569000	0.808000	0.232000	0.352000	0.154000	0.090000	Mid
99	100	Nepal	4.913000	0.446000	1.226000	0.677000	0.439000	0.285000	0.089000	Mid
100	101	Jordan	4.906000	0.837000	1.225000	0.815000	0.383000	0.110000	0.130000	Mid
101	102	Benin	4.883000	0.393000	0.437000	0.397000	0.349000	0.175000	0.082000	Mid
102	103	Congo (Brazzaville)	4.812000	0.673000	0.799000	0.508000	0.372000	0.105000	0.093000	Mid
103	104	Gabon	4.799000	1.057000	1.183000	0.571000	0.295000	0.043000	0.055000	Mid
104	105	Laos	4.796000	0.764000	1.030000	0.551000	0.547000	0.266000	0.164000	Mid
105	106	South Africa	4.722000	0.960000	1.351000	0.469000	0.389000	0.130000	0.055000	Mid
106	107	Albania	4.719000	0.947000	0.848000	0.874000	0.383000	0.178000	0.027000	Mid
107	108	Venezuela	4.707000	0.960000	1.427000	0.805000	0.154000	0.064000	0.047000	Mid
108	109	Cambodia	4.700000	0.574000	1.122000	0.637000	0.609000	0.232000	0.062000	Mid
109	110	Palestinian Territories	4.696000	0.657000	1.247000	0.672000	0.225000	0.103000	0.066000	Mid
110	111	Senegal	4.681000	0.450000	1.134000	0.571000	0.292000	0.153000	0.072000	Mid
111	112	Somalia	4.668000	0.000000	0.698000	0.268000	0.559000	0.243000	0.270000	Mid
112	113	Namibia	4.639000	0.879000	1.313000	0.477000	0.401000	0.070000	0.056000	Mid
113	114	Niger	4.628000	0.138000	0.774000	0.366000	0.318000	0.188000	0.102000	Mid
114	115	Burkina Faso	4.587000	0.331000	1.056000	0.380000	0.255000	0.177000	0.113000	Mid
115	116	Armenia	4.559000	0.850000	1.055000	0.815000	0.283000	0.095000	0.064000	Mid
116	117	Iran	4.548000	1.100000	0.842000	0.785000	0.305000	0.270000	0.125000	Mid
117	118	Guinea	4.534000	0.380000	0.829000	0.375000	0.332000	0.207000	0.086000	Mid
118	119	Georgia	4.519000	0.886000	0.666000	0.752000	0.346000	0.043000	0.164000	Mid
119	120	Gambia	4.516000	0.308000	0.939000	0.428000	0.382000	0.269000	0.167000	Mid
120	121	Kenya	4.509000	0.512000	0.983000	0.581000	0.431000	0.372000	0.053000	Mid
121	122	Mauritania	4.490000	0.570000	1.167000	0.489000	0.066000	0.106000	0.088000	Low
122	123	Mozambique	4.466000	0.204000	0.986000	0.390000	0.494000	0.197000	0.138000	Low
123	124	Tunisia	4.461000	0.921000	1.000000	0.815000	0.167000	0.059000	0.055000	Low
124	125	Bangladesh	4.456000	0.562000	0.928000	0.723000	0.527000	0.166000	0.143000	Low
125	126	Iraq	4.437000	1.043000	0.980000	0.574000	0.241000	0.148000	0.089000	Low
126	127	Congo (Kinshasa)	4.418000	0.094000	1.125000	0.357000	0.269000	0.212000	0.053000	Low
127	128	Mali	4.390000	0.385000	1.105000	0.308000	0.327000	0.153000	0.052000	Low
128	129	Sierra Leone	4.374000	0.268000	0.841000	0.242000	0.309000	0.252000	0.045000	Low
129	130	Sri Lanka	4.366000	0.949000	1.265000	0.831000	0.470000	0.244000	0.047000	Low
130	131	Myanmar	4.360000	0.710000	1.181000	0.555000	0.525000	0.566000	0.172000	Low
131	132	Chad	4.350000	0.350000	0.766000	0.192000	0.174000	0.198000	0.078000	Low
132	133	Ukraine	4.332000	0.820000	1.390000	0.739000	0.178000	0.187000	0.010000	Low
133	134	Ethiopia	4.286000	0.336000	1.033000	0.532000	0.344000	0.209000	0.100000	Low
134	135	Swaziland	4.212000	0.811000	1.149000	0.000000	0.313000	0.074000	0.135000	Low
135	136	Uganda	4.189000	0.332000	1.069000	0.443000	0.356000	0.252000	0.060000	Low
136	137	Egypt	4.166000	0.913000	1.039000	0.644000	0.241000	0.076000	0.067000	Low
137	138	Zambia	4.107000	0.578000	1.058000	0.426000	0.431000	0.247000	0.087000	Low
138	139	Togo	4.085000	0.275000	0.572000	0.410000	0.293000	0.177000	0.085000	Low
139	140	India	4.015000	0.755000	0.765000	0.588000	0.498000	0.200000	0.085000	Low
140	141	Liberia	3.975000	0.073000	0.922000	0.443000	0.370000	0.233000	0.033000	Low
141	142	Comoros	3.973000	0.274000	0.757000	0.505000	0.142000	0.275000	0.078000	Low
142	143	Madagascar	3.933000	0.274000	0.916000	0.555000	0.148000	0.169000	0.041000	Low
143	144	Lesotho	3.802000	0.489000	1.169000	0.168000	0.359000	0.107000	0.093000	Low
144	145	Burundi	3.775000	0.046000	0.447000	0.380000	0.220000	0.176000	0.180000	Low
145	146	Zimbabwe	3.663000	0.366000	1.114000	0.433000	0.361000	0.151000	0.089000	Low
146	147	Haiti	3.597000	0.323000	0.688000	0.449000	0.026000	0.419000	0.110000	Low
147	148	Botswana	3.488000	1.041000	1.145000	0.538000	0.455000	0.025000	0.100000	Low
148	149	Syria	3.462000	0.619000	0.378000	0.440000	0.013000	0.331000	0.141000	Low
149	150	Malawi	3.410000	0.191000	0.560000	0.495000	0.443000	0.218000	0.089000	Low
150	151	Yemen	3.380000	0.287000	1.163000	0.463000	0.143000	0.108000	0.077000	Low
151	152	Rwanda	3.334000	0.359000	0.711000	0.614000	0.555000	0.217000	0.411000	Low
152	153	Tanzania	3.231000	0.476000	0.885000	0.499000	0.417000	0.276000	0.147000	Low
153	154	Afghanistan	3.203000	0.350000	0.517000	0.361000	0.000000	0.158000	0.025000	Low
154	155	Central African Republic	3.083000	0.026000	0.000000	0.105000	0.225000	0.235000	0.035000	Low
155	156	South Sudan	2.853000	0.306000	0.575000	0.295000	0.010000	0.202000	0.091000	Low

	Country or region	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption
0	Canada	7.278	1.365	1.505	1.039	0.584	0.285	0.308
1	US	6.892	1.433	1.457	0.874	0.454	0.280	0.128
2	UK	7.054	1.333	1.538	0.996	0.450	0.348	0.278
3	India	4.015	0.755	0.765	0.588	0.498	0.200	0.085